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Found 3 papers, 2 papers with code
Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Training
Our third contribution is a novel self-supervised training approach that uses our certificate of observable correctness to provide the supervisory signal to C-3PO during training.
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks
In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies.
Neural Trees for Learning on Graphs
We also prove that the number of parameters needed to achieve an $\epsilon$-approximation of the distribution function is exponential in the treewidth of the input graph, but linear in its size.
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